Impact of distance measures in adaptive K-means clustering on load profiles and spatial patterns of distributed substations in Thailand.

To address the challenges of increasing electricity demand and diverse consumption behavior, this study explored an adaptive K-means clustering approach for segmenting 24-h load profiles from 627 distributed substations of the Provincial Electricity Authority (PEA) in Thailand. Euclidean distance and Cosine similarity were applied as distance measures, with cluster numbers (K) ranging from 2 to 10. Clustering validity was evaluated using the Davies-Bouldin Index (DBI), Calinski-Harabasz Index (CHI), and Silhouette Coefficient (SC), alongside three proposed metrics: Averaged Standard Deviation (ASD), Standard Deviation of Derived Slope (SDDS), and Absolute Different Area (ADA). Euclidean distance was found to be more effective in clustering load profiles based on the magnitude of electricity consumption, while Cosine similarity better captured the shape and temporal patterns of usage, as supported by the proposed metrics. Optimal clustering for the distributed substations of PEA was achieved with K equal to 3 or 4, balancing simplicity and detail. The spatial distribution of substation clusters across different regions in Thailand revealed distinct energy consumption patterns linked to customer sectors. These findings provide valuable insights for electricity management strategies, distribution grid infrastructure planning, and future energy policy development in Thailand.